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xc-llm-ascend/tests/ut/quantization/test_w4a4_flatquant_dynamic.py
Cao Yi a69ef10c3a [Refactor] Quantization Module Refactor (#5738) 
### Summary

This PR refactors the `vllm_ascend/quantization` module to improve code
organization, maintainability, and extensibility. The refactoring
introduces a clear separation of concerns with a registry-based scheme
discovery pattern, abstract base classes for quantization schemes, and
dedicated wrapper classes.

### Key Changes

#### 1. **Modular Directory Structure**

| Before | After |
|--------|-------|
| Flat file structure with mixed responsibilities | Organized into
`methods/` subpackage for schemes |
| Single `quant_config.py` (600+ lines) | Separate config files:
`modelslim_config.py`, `compressed_tensors_config.py` |
| `utils.py` with scheme lookup logic | `methods/registry.py` with
decorator-based registration |

#### 2. **Registry-Based Scheme Discovery**

Replaced hardcoded `ASCEND_QUANTIZATION_METHOD_MAP` dictionary with a
decorator-based registry pattern:

```python
# Before: Manual dictionary mapping
ASCEND_QUANTIZATION_METHOD_MAP = {
    "W8A8_DYNAMIC": {"linear": AscendW8A8DynamicLinearMethod, ...},
    ...
}

# After: Decorator-based registration
@register_scheme("W8A8_DYNAMIC", "linear")
class AscendW8A8DynamicLinearMethod(AscendLinearScheme):
    ...
```

#### 3. **Abstract Base Classes**

Introduced three abstract base classes in `methods/base.py`:
- `AscendLinearScheme` - Base for linear layer quantization
- `AscendMoEScheme` - Base for MoE layer quantization  
- `AscendAttentionScheme` - Base for attention layer quantization

#### 4. **Separated Config and Wrapper Classes**

- **Config classes** (`AscendModelSlimConfig`,
`AscendCompressedTensorsConfig`): Handle config parsing and scheme
selection
- **Wrapper classes** (`AscendLinearMethod`, `AscendFusedMoEMethod`,
etc.): Implement vLLM interfaces and delegate to schemes

#### 5. **Cleaner Public API**

```python
# New clean module interface
from vllm_ascend.quantization import (
    AscendModelSlimConfig,
    AscendCompressedTensorsConfig,
)
from vllm_ascend.quantization.methods import get_scheme_class
```

### Architecture Diagram

```mermaid
classDiagram
    direction TB
    
    class QuantizationConfig {
        <<vLLM Interface>>
        +get_quant_method()
    }
    
    class AscendModelSlimConfig {
        +quant_description
        +get_quant_method()
        -create_scheme_for_layer()
    }
    
    class AscendCompressedTensorsConfig {
        +target_scheme_map
        +get_quant_method()
        -_get_scheme_from_parts()
    }
    
    class AscendLinearMethod {
        <<Wrapper>>
        +quant_method: AscendLinearScheme
        +create_weights()
        +apply()
    }
    
    class AscendFusedMoEMethod {
        <<Wrapper>>
        +quant_method: AscendMoEScheme
        +create_weights()
        +apply()
    }
    
    class AscendLinearScheme {
        <<Abstract>>
        +get_weight()*
        +apply()*
        +get_pertensor_param()
        +get_perchannel_param()
    }
    
    class AscendMoEScheme {
        <<Abstract>>
        +get_weight()*
        +get_dynamic_quant_param()*
        +apply()*
    }
    
    class W8A8DynamicLinear {
        +get_weight()
        +apply()
    }
    
    class W8A8DynamicMoE {
        +get_weight()
        +apply()
    }
    
    QuantizationConfig <|-- AscendModelSlimConfig
    QuantizationConfig <|-- AscendCompressedTensorsConfig
    
    AscendModelSlimConfig ..> AscendLinearMethod : creates
    AscendModelSlimConfig ..> AscendFusedMoEMethod : creates
    AscendCompressedTensorsConfig ..> AscendLinearMethod : creates
    AscendCompressedTensorsConfig ..> AscendFusedMoEMethod : creates
    
    AscendLinearMethod o-- AscendLinearScheme : delegates to
    AscendFusedMoEMethod o-- AscendMoEScheme : delegates to
    
    AscendLinearScheme <|-- W8A8DynamicLinear
    AscendMoEScheme <|-- W8A8DynamicMoE
```

### Scheme Registration Flow

```mermaid
sequenceDiagram
    participant Module as Scheme Module
    participant Registry as _SCHEME_REGISTRY
    participant Config as QuantConfig
    participant Wrapper as Wrapper Class
    
    Note over Module: At import time
    Module->>Registry: @register_scheme("W8A8_DYNAMIC", "linear")
    Registry->>Registry: Store (quant_type, layer_type) -> Class
    
    Note over Config: At runtime
    Config->>Config: Determine quant_type from description
    Config->>Registry: get_scheme_class(quant_type, layer_type)
    Registry-->>Config: Return scheme class
    Config->>Config: scheme = scheme_cls()
    Config->>Wrapper: Create wrapper with scheme
    Wrapper-->>Config: Return wrapper instance
```

### File Changes Summary

| Original Files | Refactored Files |
|----------------|------------------|
| `__init__.py` (empty) | `__init__.py` (exports public API) |
| `quant_config.py` | `modelslim_config.py` + `wrappers.py` |
| `compressed_tensors/` | `compressed_tensors_config.py` |
| `utils.py` | `methods/registry.py` |
| `w8a8_dynamic.py` | `methods/w8a8_dynamic.py` |
| `w8a8.py` | `methods/w8a8_static.py` |
| `w4a4_flatquant_dynamic.py` | `methods/w4a4_flatquant.py` |
| ... | `methods/base.py` (new) |

### Benefits

1. **Extensibility**: Adding new quantization schemes only requires
implementing the base class and adding `@register_scheme` decorator
2. **Maintainability**: Clear separation between config parsing, wrapper
logic, and scheme implementation
3. **Testability**: Abstract base classes enable easier unit testing and
mocking
4. **Discoverability**: Registry pattern makes it easy to list all
supported schemes
5. **Reduced Coupling**: Config classes no longer need to know about all
scheme implementations

___

- vLLM version: v0.13.0
- vLLM main:
2f4e6548ef

---------

Signed-off-by: SlightwindSec <slightwindsec@gmail.com>
2026-01-23 14:13:47 +08:00

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import unittest
from unittest.mock import MagicMock, patch
import torch
import torch.nn as nn
from vllm_ascend.quantization.methods.w4a4_flatquant import (
AscendW4A4FlatQuantDynamicLinearMethod, get_decompose_dim,
pack_int4_weights)
class TestW4A4FlatQuantDynamic(unittest.TestCase):
"""
Unit test suite for AscendW4A4FlatQuantDynamicLinearMethod and its helper functions.
"""
def setUp(self):
"""Set up the test environment before each test."""
self.method = AscendW4A4FlatQuantDynamicLinearMethod()
self.output_size = 64
self.input_size = 768 # 768 = 24 * 32, divisible by 8
self.params_dtype = torch.float16
## Test Helper Functions
## --------------------
def test_get_decompose_dim(self):
"""
Tests the get_decompose_dim function with various inputs.
"""
self.assertEqual(get_decompose_dim(1024), (32, 32))
self.assertEqual(get_decompose_dim(768), (24, 32))
self.assertEqual(get_decompose_dim(100), (10, 10))
self.assertEqual(get_decompose_dim(99), (9, 11))
@patch('vllm_ascend.quantization.methods.w4a4_flatquant.torch_npu')
def test_pack_int4_weights_npu_success(self, mock_torch_npu):
"""
Tests weight packing using the mocked NPU kernel.
"""
weight_tensor = torch.randn(self.output_size, self.input_size)
mock_packed_tensor = torch.randint(
0,
100, (self.output_size, self.input_size // 8),
dtype=torch.int32)
mock_npu_tensor = MagicMock()
mock_npu_tensor.to.return_value = mock_packed_tensor
mock_torch_npu.npu_convert_weight_to_int4pack.return_value = mock_npu_tensor
with patch('torch.Tensor.npu', return_value=weight_tensor):
result = pack_int4_weights(weight_tensor)
mock_torch_npu.npu_convert_weight_to_int4pack.assert_called_once()
self.assertTrue(torch.equal(result, mock_packed_tensor))
## Test AscendW4A4FlatQuantDynamicLinearMethod Class
## --------------------------------------------------
def test_get_weight(self):
"""Tests the get_weight static method for correct output."""
params = self.method.get_weight(self.input_size, self.output_size,
self.params_dtype)
self.assertIn("weight", params)
self.assertEqual(params["weight"].shape,
(self.output_size, self.input_size))
self.assertEqual(params["weight"].dtype, torch.int8)
self.assertEqual(AscendW4A4FlatQuantDynamicLinearMethod.input_size,
self.input_size)
def test_get_weight_value_error(self):
"""Tests that get_weight raises ValueError for invalid input_size."""
with self.assertRaisesRegex(ValueError, "must be divisible by 8"):
self.method.get_weight(127, self.output_size, self.params_dtype)
def test_get_pertensor_param(self):
"""Tests the get_pertensor_param static method."""
self.method.get_weight(self.input_size, self.output_size,
self.params_dtype)
params = self.method.get_pertensor_param(self.params_dtype)
left_dim, right_dim = get_decompose_dim(self.input_size)
self.assertIn("left_trans", params)
self.assertIn("right_trans", params)
self.assertIn("clip_ratio", params)
self.assertEqual(params["left_trans"].shape, (left_dim, left_dim))
self.assertEqual(params["right_trans"].shape, (right_dim, right_dim))
self.assertEqual(params["clip_ratio"].shape, (1, ))
self.assertEqual(params["left_trans"].dtype, self.params_dtype)
self.assertEqual(params["clip_ratio"].dtype, torch.float32)
def test_get_perchannel_param(self):
"""Tests the get_perchannel_param static method."""
params = self.method.get_perchannel_param(self.output_size,
self.params_dtype)
self.assertIn("weight_scale", params)
self.assertIn("weight_offset", params)
self.assertEqual(params["weight_scale"].shape, (self.output_size, 1))
self.assertEqual(params["weight_offset"].shape, (self.output_size, 1))
self.assertEqual(params["weight_scale"].dtype, torch.float32)
self.assertEqual(params["weight_offset"].dtype, torch.float32)
def test_get_pergroup_param(self):
"""Tests the get_pergroup_param method."""
params = self.method.get_pergroup_param(self.input_size,
self.output_size,
self.params_dtype)
self.assertEqual(params, {})
def _prepare_apply_mocks_and_layer(self, batch_size):
"""Helper to create a mock layer and input tensor for apply tests."""
layer = nn.Module()
m, n = get_decompose_dim(self.input_size)
layer.left_trans = torch.randn(m, m, dtype=self.params_dtype)
layer.right_trans = torch.randn(n, n, dtype=self.params_dtype)
layer.aclnn_clip_ratio = 0.95
layer.weight_packed = torch.randint(
-8, 7, (self.output_size, self.input_size // 8), dtype=torch.int32)
layer.weight_scale = torch.randn(self.output_size,
1,
dtype=torch.float32)
x = torch.randn(batch_size, self.input_size, dtype=self.params_dtype)
return layer, x, m, n
@patch('vllm_ascend.quantization.methods.w4a4_flatquant.torch_npu')
def test_apply_small_batch(self, mock_torch_npu):
"""Tests the apply method with a batch size smaller than MAX_BATCH_SIZE."""
batch_size = 128
layer, x, m, n = self._prepare_apply_mocks_and_layer(batch_size)
mock_quant_x = torch.randint(0,
255, (batch_size, self.input_size // 8),
dtype=torch.int32)
mock_act_scale = torch.randn(batch_size, 1, dtype=torch.float32)
mock_torch_npu.npu_kronecker_quant.return_value = (mock_quant_x.view(
batch_size, m, n // 8), mock_act_scale)
mock_output = torch.randn(batch_size,
self.output_size,
dtype=self.params_dtype)
mock_torch_npu.npu_quant_matmul.return_value = mock_output
bias = torch.randn(self.output_size, dtype=self.params_dtype)
output = self.method.apply(layer, x, bias=bias)
mock_torch_npu.npu_kronecker_quant.assert_called_once()
mock_torch_npu.npu_quant_matmul.assert_called_once()
self.assertTrue(
torch.allclose(output, mock_output + bias.to(self.params_dtype)))
self.assertEqual(output.shape, (batch_size, self.output_size))
@patch(
'vllm_ascend.quantization.methods.w4a4_flatquant.KRONECKER_QUANT_MAX_BATCH_SIZE',
10)
@patch('vllm_ascend.quantization.methods.w4a4_flatquant.torch_npu')
def test_apply_large_batch(self, mock_torch_npu):
"""Tests the apply method with a batch size larger than MAX_BATCH_SIZE."""
batch_size = 25
layer, x, m, n = self._prepare_apply_mocks_and_layer(batch_size)
mock_quant_x = torch.randint(0,
255, (batch_size, self.input_size // 8),
dtype=torch.int32)
mock_act_scale = torch.randn(batch_size, 1, dtype=torch.float32)
mock_torch_npu.npu_kronecker_quant.side_effect = [
(mock_quant_x[:10].view(10, m, n // 8), mock_act_scale[:10]),
(mock_quant_x[10:20].view(10, m, n // 8), mock_act_scale[10:20]),
(mock_quant_x[20:].view(5, m, n // 8), mock_act_scale[20:]),
]
mock_output = torch.randn(batch_size,
self.output_size,
dtype=self.params_dtype)
mock_torch_npu.npu_quant_matmul.return_value = mock_output
output = self.method.apply(layer, x, bias=None)
self.assertEqual(mock_torch_npu.npu_kronecker_quant.call_count, 3)
mock_torch_npu.npu_quant_matmul.assert_called_once()
self.assertTrue(torch.equal(output, mock_output))
self.assertEqual(output.shape, (batch_size, self.output_size))
def test_apply_dimension_mismatch_error(self):
"""Tests that apply raises ValueError on transform matrix dimension mismatch."""
layer, x, _, _ = self._prepare_apply_mocks_and_layer(16)
layer.left_trans = torch.randn(20, 20)
layer.right_trans = torch.randn(30, 30) # 20 * 30 != 768
with self.assertRaisesRegex(
ValueError, "FlatQuant transform matrices dimension mismatch"):
self.method.apply(layer, x)
@patch('vllm_ascend.quantization.methods.w4a4_flatquant.pack_int4_weights')
def test_process_weights_after_loading(self, mock_pack_weights):
"""Tests weight processing after loading, without transpose."""
layer = nn.Module()
layer.weight = torch.randint(-8,
7, (self.output_size, self.input_size),
dtype=torch.int8)
layer.weight_scale = torch.randn(self.output_size,
1,
dtype=torch.bfloat16)
layer.weight_offset = torch.randn(self.output_size,
1,
dtype=torch.bfloat16)
layer.left_trans = torch.randn(24, 24)
layer.right_trans = torch.randn(32, 32)
layer.clip_ratio = torch.tensor([0.9])
mock_packed = torch.randint(0,
100,
(self.output_size, self.input_size // 8),
dtype=torch.int32)
mock_pack_weights.return_value = mock_packed
self.method.process_weights_after_loading(layer)
mock_pack_weights.assert_called_once()
self.assertFalse(hasattr(layer, 'weight'))
self.assertTrue(hasattr(layer, 'weight_packed'))
self.assertTrue(torch.equal(layer.weight_packed.data, mock_packed))
self.assertEqual(layer.weight_scale.dtype, torch.float32)
self.assertEqual(layer.weight_offset.dtype, torch.float32)
self.assertEqual(layer.clip_ratio.dtype, torch.float32)
self.assertTrue(layer.aclnn_clip_ratio - 0.9 < 0.01)
self.assertEqual(layer.left_trans.shape, (24, 24))
self.assertTrue(layer.left_trans.is_contiguous())
if __name__ == '__main__':
unittest.main(argv=['first-arg-is-ignored'], exit=False)
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